An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression - Lu, Zhufeng; Zhang, Xiaodong; Li, Hanzhe; Zhang, Teng; Gu, Linxia; Tao, Qing (Frontiers in Neuroscience, 2022)
In this study, an asynchronous artifact-enhanced EEG-based control paradigm assisted by slight-facial-expressions (sFE-paradigm) was developed. The brain connectivity analysis was conducted to reveal the dynamic directional interactions among brain regions under sFE-paradigm. The component analysis was applied to estimate the dominant components of sFE-EEG and guide the signal processing. Enhanced by the artifact within the detected electroencephalogram (EEG), sFE-paradigm focused on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm contained four steps, including ‘obvious non-sFE-EEGs exclusion’, ‘interface ‘ON’ detection’, ‘sFE-EEGs real-time decoding’, and ‘validity judgment’. It provided the asynchronous function, decoded 8 instructions from the latest 100 ms signal, and greatly reduced the frequent misoperation. In the offline assessment, sFE-paradigm achieved 96.46%±1.07 accuracy for interface ‘ON' detection and 92.68%±1.21 for sFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This sFE-paradigm was applied to two online manipulations for evaluating stability and agility. In ‘object-moving with a robotic arm’, the averaged intersection-over-union was 60.03±11.53%. In ‘water-pouring with a prosthetic hand’, the average water volume was 202.5±7.0 ml. During online, sFE-paradigm performed no significant difference (P = 0.6521 & P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of sFE-paradigm, enabling a novel solution to the noninvasive EEG-based control in real-world challenges.